Training: In a single training iteration, we first update the discriminators \(D_{1}\)  and \(D_{2}\)  to maximize the losses in Eq. REF  and REF .
We update the discriminators 5 times for every update of the rest of the functions (\(f, g, h\) ), as in WGAN [1]}. Following this, the parameters \(\theta _{f}, \theta _{g}, \theta _{h}, \theta _{c}\)  corresponding to \(f, g\) , \(h\)  and class centers, respectively, are updated to minimize:
